The classification of wink-based eeg signals by means of transfer learning models

Stroke is one of the dominant causes of impairme nt. An estimation of half post-stroke survivors suffer from a severe motor or cognitive deterioration, that affects the functionality of the affected parts of the body, which in turn, prevents the patients from carrying out Activities of Daily Living (ADL). EEG signals which contains information on the activities carried out by a human that is widely used in many applications of BCI technologies which offers a means of controlling exoskeletons or automated orthosis to facilitate their ADL. Although motor imagery signals have been used in assisting the hand grasping motion amongst others motions, nonetheless, such signals are often difficult to be generated. It is non-trivial to note that EEG-based signals for instance, winking could mitigate the aforesaid issue. Nevertheless, extracting and attaining significant features from EEG signals are also somewhat challenging. The utilization of deep learning, particularly Transfer Learning (TL), have been demonstrated in the literature to b e able to provide seamless extraction of such signals in a myria d of various applications. Hitherto, limited studies have investigated the classification of wink-based EEG signals through TL accompanied by classical Machine Learning (ML) pipelines. This study aimed to explore the performance of different pre-processing methods, namely Fast Fourier Transform, Short-Time Fourier Transform, Discrete Wavelet Transform, and Continuous Wavelet Transform (CWT) that could allow TL models to extract features from the images generated and classify through selected classical ML algorithms . These pre-processing methods were utilized to convert the digital signals into respective images of all the right and left winking EEG signals along with no winking signals that were collected from ten (6 males and 4 females, aged between 22 and 29) subjects. The implementation of pre-processing algorithms has been demonstrated to be able to mitigate the signal noises that arises from the winking signals without the need for the use signal filtering algorithms. A new form of input which consists of scalogram and spectrogram images that represents both time and frequency domains , are then introduced in the classification of wink-based EEG signals. Different TL models were exploited to extract features from the transformed EEG signals. The features extracted were then classified through three classical ML models, namely Support Vector Machine, k -Nearest Neighbour (k-NN) and Random Forest to determine the best pipeline for wink -based EEG signals. The hyperparameters of the ML models were tuned through a 5-fold crossvalidation technique via an exhaustive grid search approach. The training, validation and testing of the models were split with a stratified ratio of 60:20:20, respectively. The results obtained from the TL-ML pipelines were evaluated in terms of classification accuracy, Precision, Recall, F1-Score and confusion matrix. It was demonstrated from the simulation investigation that the CWT model could yield a better signal transformation amongst the preprocessing algorithms. In addition, amongst the eighteen TL models evaluated based on the CWT transformation, fourteen was f ound to be able to extract the features reasonable, i.e., VGG16, VGG19, ResNet101, ResNet101 V2, ResNet152, ResNet152 V2, Inception V3, Inception ResNet V2, Xception, MobileNetV2, DenseNet 121, DenseNet 169, NasNetMobile and NasNetLarge. Whilst it was observed that the optimized k-NN model based on the aforesaid pipeline could achieve a classification accuracy of 100% for the training, validation, and tes t data. Nonetheless, upon carrying out a robustness test on new data, it was demonstrated that the CWT-NasNetMobile-kNN pipeline yielded the best performance. Therefore, it could be concluded that the proposed CWT-NasNetMobile-k-NN pipeline is suitable to be adopted to classify -winkbased EEG signals for BCI applications,for instance a grasping exoskeleton

The Classification of Wink-Based EEG Signals: The identification on efficiency of transfer learning models by means of kNN classifier

One of the earliest methods to observe the brain dynamic is through Electroencephalogram (EEG) brain signal. It is widely known as a non-invasive, reliable, and affordable way of recording the brain activities. It has become the most wanted way of diagnosis and treatment for mental and brain neurogenerative diseases and abnormalities. It also one of the most appropriate signals in Brain-Computer Interfaces (BCI) applications. BCI frequently used by neuromuscular disorder (post-stroke) patients to aid them in activities of daily living (ADL). In this study, the adequacy of various TL models, i.e., NasNetMobile, and NasNetLarge in extracting features to classify wink-based EEG signals were investigated. The time-frequency scalogram conversion of the Right Wink, Left Wink, and No Wink based on EEG signals was carried out through Continuous Wavelet Transform (CWT) algorithm. The features that were extracted through Transfer Learning (TL) models were fed into a number of k-Nearest Neighbors (kNN) classifier models to determine the performance of various feature extraction methods to classify the winking signals. The input data are divided into training, validation, and testing datasets via a stratified ratio of 60:20:20. It was shown through this study, that the features extracted by means of NasNetLarge were more efficient compared with NasNetMobile. The Classification Accuracy (CA) of training dataset through NasNetLarge pipeline is 98% which was higher compared to NasNetMobile through the kNN model which consists of k-value of 2 and Minkowski Distance. The validation and testing CA attained through NasNetMobile and NasNetLarge models are 100%. Therefore, it could be concluded that the proposed pipeline which consists of CWT-NasNetLarge-kNN is suitable to be adopted to classify wink-based EEG signals for different BCI applications

The Classification of Hallucination: The Identification of Significant Time-Domain EEG Signals

Electroencephalogram (EEG) has now become one of the means in the medical sector to detect hallucination. The main objective of this study is to classify the onset of hallucination via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features that could yield high classification accuracy (CA) on different classifiers. Emotiv Insight, a 5 channels headset, was used to record the EEG signal of 5 subjects aged between 23 and 27 years old when they are in a hallucination state. Eight statistical-based features, i.e., mean, standard deviation, variance, median, minimum, maximum, kurtosis, skewness and standard error mean from each channel. The identification of the significant features is obtained via Extremely Randomised Trees. The classification performance of all features, as well as selected features, are evaluated through, i.e. Random Forest (RF), k-Nearest Neighbours (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Logistic Regression (LR). The dataset was separated into the ratio of 70:30 for training and testing data. It was shown from the study, that the LR classifier is able to provide excellent CA on both the train and test dataset by considering the identified significant features. The identification of such features is non-trivial towards classifying the onset of hallucination in real-time as the computational expense could be significantly reduced

The classification of blinking: an evaluation of significant time-domain features

Stroke is one of the most widespread causes of disability-adjusted life-years (DALYs). EEG-based Brain-Computer Interface (BCI) system is a potential solution for the patients to help them regain their mobility. The study aims to classify eye blinks through features extracted from time-domain EEG signals. Six features (mean, standard deviation, root mean square, skewness, kurtosis and peak-to-peak) from five channels (AF3, AF4, T7, T8 and Pz) were collected from five healthy subjects (three male and two female) aged between 22 and 24. The Chi-square (χ2) method was used to identify significant features. Six machine learning models, i.e. Support Vector Machine (SVM)), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and Artificial Neural Networks (ANN), were developed based on all the extracted features as well as the identified significant features. The training and test datasets were divided into a ratio of 70:30. It is shown that the classification accuracy of the evaluated classifiers by considering the fifteen features selected through the Chi-square is comparable to that of the selection of all features. The highest classification accuracy was demonstrated via the RF classifier for both cases. The findings suggest that even that with a reduced feature set, a reasonably high classification accuracy could be achieved, i.e., 91% on the test set. This observation further implies the viable implementation of BCI applications with a reduced computational expense

The power level control of a pressurised water reactor nuclear power plant

The control system of a reactor core in a Nuclear Power Plant (NPP) is non-trivial to ensure safe operation of a nuclear power plant. Owing to the complex and non-linear characteristics of a nuclear power plant, it is, therefore, essential to control the power in load following condition through the regulation of the reactor core. The aim of this paper is to evaluate the efficacy of different variation of classical control schemes, namely, P, PI, PD and PID to control the power level output. The reactor core model is based on the H.B. Robinson Pressurised Water Reactor NPP. The control schemes evaluated were tuned based on the Ziegler-Nichols tuning method. It was demonstrated through the following simulation investigation that the PID control scheme is appropriate in regulating the power level

An evaluation of different fast fourier transform - transfer learning pipelines for the classification of wink-based EEG signals

Brain Computer-Interfaces (BCI) offers a means of controlling prostheses for neurological disorder patients, primarily owing to their inability to control such devices due to their inherent physical limitations. More often than not, the control of such devices exploits the use of Electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features is often a laborious undertaking. The use of Transfer Learning (TL) has been demonstrated to be able to mitigate the issue. However, the employment of such a method towards BCI applications, particularly with regards to EEG signals are limited. The present study aims to assess the effectiveness of a number of DenseNet TL models, viz. DenseNet169, DenseNet121 and DenseNet201 in extracting features for the classification of wink-based EEG signals. The extracted features are then classified through an optimised Random Forest (RF) classifier. The raw EEG signals are transformed into a spectrogram image via Fast Fourier Transform (FFT) before it was fed into selected TL models. The dataset was split with a stratified ratio of 60:20:20 into train, test, and validation datasets, respectively. The hyperparameters of the RF model was optimised through the grid search approach that utilises the five-fold cross-validation technique. It was established from the study that amongst the DenseNet pipelines evaluated, the DenseNet169 performed the best with an overall validation and test accuracy of 89%. The findings of the present investigation could facilitate BCI applications, e.g., for a grasping exoskeleton

The Classification of Wink-Based EEG Signals: The Identification of Significant Time-Domain Features

Brain-Computer Interface (BCI) has become popular with physically challenged individuals, particularly in enhancing their activities of daily living. Electroencephalogram (EEG) signals are used to control BCI-based devices. Nonetheless, it is worth noting that the use of a multitude of features may impede the real-time execution of BCI devices. The present study aims at identifying significant time-domain based features that could provide a reasonable classification of the right or left wink based on EEG signals evoked by the aforesaid facial expressions. The Emotiv Insight mobile EEG system was used to capture the EEG signals acquired from the winking of the left and right eye of five healthy subjects between the age of 23 and 27 years old. Nine statistical time-domain based features were extracted, namely maximum (Max), minimum (Min), mean, median, standard deviation (SD), variance, skewness, kurtosis, and root mean square (RMS) on five channels. An ensemble learning method, i.e. Extremely Randomised Trees, was used to identify the significant features. The feature selection effect towards wink classification was evaluated via the k-Nearest Neighbours (k-NN) classifier. The training to test ratio of the extracted signals was set to 70:30. It was shown from the study, that five features were found to be significant, viz. Max_AF4, SD_AF4, skewness_AF3, kurtosis_AF4 and kurtosis_AF3, respectively. The training classification accuracy (CA) by considering all features and selected features was ascertained to be both 100%, respectively, whilst, the test CA was also found to be identical for both models with no misclassification transpired. Therefore, it could be established from the study that a comparable classification efficacy is attainable through the identification of significant features. The findings are non-trivial, particularly with respect to the implementation of the developed classifier in real-time

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality

The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline

Brain–Computer Interface technology plays a vital role in facilitating post-stroke patients’ ability to carry out their daily activities of living. The extraction of features and the classification of electroencephalogram (EEG) signals are pertinent parts in enabling such a system. This research investigates the efficacy of Transfer Learning models namely ResNet50 V2, ResNet101 V2, and ResNet152 V2 in extracting features from CWT converted wink-based EEG signals, prior to its classification via a fine-tuned Support Vector Machine (SVM) classifier. It was shown that ResNet152 V2-SVM pipeline could achieve an excellent accuracy on all train, test and validation datasets